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Revealing Information while Preserving Privacy

Summary: Model DB as an n-bit vector and give a polynomial-time reconstruction algorithm that recovers the data from noisy subset-sum answers, showing privacy is violated unless noise is Ω(√n). Tightness: exhibit access schemes with Õ(√n) noise; for time-T adversaries required noise ≈√T. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1291
Venue
PODS
Year
2003
Pagerank
0.00031082693
Overall Rank
123 | 99.16%
DOI
10.1145/773153.773173

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{dinur_pods03,
        address = {New York, NY, USA},
        series = {{PODS} '03},
        title = {{Revealing Information while Preserving Privacy}},
        url = {https://dl.acm.org/doi/10.1145/773153.773173},
        doi = {10.1145/773153.773173},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Dinur, Irit and Nissim, Kobbi},
        year = {2003}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
70 Privacy-Preserving Data Mining 2000 SIGMOD 0.0003804755
244 On the Design and Quantification of Privacy Preserving Data Mining Algorithms 2001 PODS 0.00023476901
249 Statistical Databases: Characteristics, Problems, and Some Solutions 1982 VLDB 0.00023275528
1,183 An Analytic Approach to Statistical Databases 1983 VLDB 0.00011778053
2,017 Auditing Boolean Attributes 2000 PODS 9.3006188e-05
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